Classification of Learning Sentiments of College Students Based on Topic Discussion Texts of Online Learning Platforms

Abstract

Depicting the online learning process of student users from multiple angles can help implement deep learning and effectively improve their online learning quality, and it’s a practical and very meaningful work to mine the data burying in the topic discussion texts of online learning platforms so that useful information could be extracted and attained to help teachers better understand students’ learning sentiments and assist students to know of the learning status of their peers. However, in existing conventional sentiment analysis methods, the sample data with uncategorized tags are still labelled manually, and such work is usually time consuming and inefficient. In view of these defects, this paper aims to study the classification of college students’ learning sentiments based on the topic discussion texts of online learning platforms. In the beginning, this paper gave the overall structure of the proposed college student Learning Sentiment Classification (LSC) algorithm, and discussed the similarity between the topic discussion content and the teaching content. Then, this paper proposed to integrate Convolution Neural Network (CNN) with the Long-Short Term Memory (LSTM) network to build the said LSC model, so as to merge the advantages of the two and improve the accuracy of learning sentiment rating. After that, embedding layers of static words and non-static words were introduced into the proposed model for the purpose of realizing the mining of specific textual information while enhancing the semantic expression ability of the words. At last, experimental results verified the effectiveness of the proposed model.